多传感器融合实现机器人精确定位.docxVIP

多传感器融合实现机器人精确定位 多传感器融合实现机器人精确定位 第28卷第2期2021年2月东北大学学报(自然科学版) Journal of Northeastern University (Natural Science ) Vol 128,No. 2Feb. 2021 多传感器融合实现机器人精确定位 吴　伟1, 刘兴刚1, 王忠实2, 徐心和1 (1. 东北大学教育部流程工业综合自动化重点实验室, 辽宁沈阳　110004; 2. 沈阳大学信息工程学院, 辽宁沈阳　110044) 摘　　　要:提出采用超声波距离扫描传感器和视觉传感器数据融合技术实现室内环境复杂特征角和半平面的提取, 以便更精确地重构环境特征?利用与坐标无关的对称扰动模型建立超声波扫描的环境特征模型、改进的扩展卡尔曼滤波估计求解?以马氏距离作为特征融合判定的依据, 并且在传感器校准时, 度和消逝线的摄像机自动校准方法, , , 较为精确的二维多边形环境地图得以重建, 关　键　词:数据融合; 对称扰动模型; ; 中图分类号:TP 242　　　A () Fusion to Precise R obot Localization W U Wei , L IU Xi ng 2gang , W A N G Zhong 2shi , X U Xi n 2he (1. K ey Laboratory of Process Industry Automation , Ministry of Education , Northeastern University , Shenyang 110004, China ; 2. School of Information Technology Management Engineering , Shenyang University , Shenyang 110044, China. Corres pondent :WU Wei , E 2mail :awuwei @. cn ) Abstract :Based on the multi 2sensor data fusion technology , a robot with ultrasonic rangefinder and CCD camera is proposed to extract the complex characteristic corners and semiplanes in an indoor environment , so as to rebuild the relevant environmental features more accurately. Environmental features observed by ultrasonic scan are defined with symmetrical perturbation model that is regardless of coordinates , and the product of their bound matrix and relative location vector is considered as the fusion observation equation. EKF (extended Kalman filter ) is used to estimate the solution to the equation with Mahalanobis distance taken as the criterion for characteristic fusion. In addition , the CCD automatic calibration based on local intensity and vanishing line is introduced to improve the calibration accuracy of horizontal boundary points , thus enhancing greatly the corner accuracy. In such a way the 2D polynomial environmental map can be replotted to lay down a solid foundation for robot localization. K ey w ords :data fusion ; symmetrical perturbation model ; CCD